Missing data is a common complication in data analysis. In many medical settings missing
data can cause difficulties in estimation, precision and inference. Multiple imputation …

Empirical data are seldom completely observed. How to adequately analyse data sets
affected by missing values is usually not the focus of courses at bachelor or master level …

Multiple imputation (MI) is one of the principled methods for dealing with missing data. In
addition, multilevel models have become a standard tool for analyzing the nested data …

Multiple imputation is a technique for handling data sets with missing values. The method
fills in the missing values several times, creating several completed data sets for analysis …

Find guidance on using SAS for multiple imputation and solving common missing data
issues. Multiple Imputation of Missing Data Using SAS provides both theoretical background …

Multiple imputation: a primer - Joseph L Schafer, 1999 Skip to main content Intended for
healthcare professionals Sage Journals Home Search this journal Search all journals Enter …

Multiple imputation is a widely recommended means of addressing the problem of missing
data in psychological research. An often-neglected requirement of this approach is that the …

Multiple imputation is an increasingly popular strategy for analyzing data with missing
values (Allison, 2002; Rubin, 1987). In multiple imputation, the analyst creates several …

This paper provides an overview of multiple imputation and current perspectives on its use
in medical research. We begin with a brief review of the problem of handling missing data in …

Conducting sample surveys, imputing incomplete observations, and analyzing the resulting
data are three indispensable phases of modern practice with public-use data files and with …